Senior Software Engineer - AI Agents

We’re hiring on behalf of a fast-growing fintech company building AI-powered financial automation tools for businesses operating globally.

This is a high-impact engineering role focused on taking internal AI agent prototypes into production-grade systems used across real financial workflows. You’ll work closely with product and engineering teams to build reliable, compliant, observable agent systems that support finance, operations, payments, and accounting-related use cases.

The ideal candidate is a strong backend or full-stack engineer with hands-on experience building AI agents in production, a solid understanding of LLMs, and the ability to make pragmatic architecture decisions across cost, latency, reliability, and safety.

The Role

You’ll help build and scale intelligent AI agents that support business finance teams by automating operational workflows, surfacing insights, and reducing manual effort.

The initial focus will be on improving an internal financial operations agent and preparing it for wider production use. From there, you’ll help expand capabilities across areas such as accounting automation, policy workflows, card operations, and broader finance automation.

This role requires someone who can think beyond prototypes. You’ll be expected to build systems that are robust, secure, measurable, and suitable for handling sensitive financial data.

Key Responsibilities

• Take AI agent prototypes from internal pilot stage to reliable production systems.
• Build observability, monitoring, evaluation, and guardrails across agent workflows.
• Design agent systems that can reason, plan, use tools, and support proactive finance operations.
• Make architectural decisions around model choice, latency, cost, safety, and scalability.
• Work closely with product stakeholders to prioritise features that drive adoption and measurable business value.
• Expand AI agent capabilities across financial operations, accounting automation, policy support, and related workflows.
• Evaluate new LLM models, tools, frameworks, and agent architectures, introducing practical improvements where appropriate.
• Ensure systems are designed with strong consideration for compliance, privacy, and sensitive data handling.
• Support and guide other engineers through technical review, architecture input, and delivery ownership.

Required Experience

• Strong, demonstrable experience in backend or full-stack software engineering.
• Strong proficiency in Python, or comparable experience with Java, Go, or Rust.
• Strong software engineering fundamentals, including distributed systems, APIs, backend architecture, and scalable platform design.
• Proven hands-on experience building AI agents or agentic systems in production.
• Practical experience beyond simple LLM prompting, including systems that reason, plan, act, and use external tools.
• Strong understanding of LLM concepts and how models work beneath the API layer.
• Ability to assess when to use agent frameworks such as LangChain, LangGraph, AutoGen, or similar tools, and when to build custom solutions.
• Familiarity with Model Context Protocol or similar approaches to agent-tool interoperability.
• Strong communication skills, with the ability to explain technical trade-offs clearly to engineering and product teams.
• Ownership mindset and the ability to lead technical decisions, guide others, and deliver outcomes.

Nice to Have

• Experience in fintech, payments, financial services, or other regulated environments.
• Experience with Node.js and TypeScript.
• Familiarity with MLOps, AI deployment workflows, and production model operations.
• Experience with multi-agent systems, memory architectures, tool use, or orchestration.
• Exposure to agent evaluation, monitoring, and observability tooling.
• Experience working with systems that handle sensitive customer or financial data.

What’s on Offer

• Senior engineering role with direct impact on AI product direction.
• Opportunity to build production AI agents in a commercially meaningful environment.
• Flexible hybrid working setup.
• Collaborative international team.
• Budget or stipend for company tools and work-related expenses.
• Strong culture of experimentation, product ownership, and continuous learning.